Communication networks transfer a variety of different types of information between service providers and customers. For example, various kinds of wireless and wired communication networks are used to interconnect computing devices (such as personal computers, workstations, mobile devices, video-enabled phones, etc.) for use in both personal and business environments.
The level of network load resulting from the network traffic (voice, video, data, etc.) in complex communication networks (e.g., in an Internet Protocol (IP)-based network) can greatly fluctuate, and trends in usage can vary over periods of time. Network operators must attempt to ensure both Quality of Service (QoS) guarantees for services provided and Service Level Agreement (SLA) requirements to customers. IP and other networks should thus be able to adapt to changing trends in network load in order to efficiently utilize resources of the network, and to ensure QoS and satisfy SLA requirements.
Physical changes to the network, such as upgrading of network link hardware, the addition of new network links, etc., that are needed to respond to changes in network load, require planning and time to implement. Due to the lag-time between the detection of changing network requirements and the implementation of an appropriate technical solution, a situation may arise where the solution that is implemented is itself outdated in view of the current network requirements. Thus, a reliable projection of network load trends is important for network operators in order to be able to optimize network investments and network services.
FIG. 1 shows an example of conventional network load projection technique. In this technique, based on aggregated network load measurements 31, future aggregated network load measurements 32 are predicted using mathematical models 33. The aggregation is typically performed over measurements carried out for different subscriptions. It has been found that since network load prediction as shown in FIG. 1 is based on aggregated network load measurements and fixed mathematical models, the preciseness of prediction is limited.